Academic articles

With the advances in the information and computing technologies, the ways the manufacturing enterprise systems are being managed are changing. More integration and adoption of the system perspective push further towards a more flattened enterprise. This, in addition to the varying levels of aggregation and details and the presence of the continuous and discrete types of behavior, created serious challenges for the use of the existing simulation tools for simulating the modern manufacturing enterprise system. The commonly used discrete event simulation (DES) techniques face difficulties in modeling such integrated systems due to increased model complexity, the lack of data at the aggregate management levels, and the unsuitability of DES to model the financial sectors of the enterprise. System dynamics (SD) has been effective in providing the needs of top management levels but unsuccessful in offering the needed granularity at the detailed operational levels of the manufacturing system. On the other hand the existing hybrid continuous-discrete tools are based on certain assumptions that do not fit the requirements of the common decision making situations in the business systems.

This paper first presents a simulation model implemented to study a specific workcenter in semiconductor manufacturing facilities (fabs) with the objective of controlling the risk on process equipment. The different components of the model, its inputs and its outputs, that led us to propose improvements in the workcenter, are explained. The risk evaluated in this study is the exposure level in the number of wafers on a process tool since the latest control performed for this tool, based on an indicator called Wafer at Risk. Our analysis shows that measures should be better managed to avoid lack of control and that an appropriate qualification strategy is required.

A virtual factory should represent most of the features and operations of the corresponding real factory. Some of the key features of the virtual factory include the ability to assess performance at multiple resolutions and generate analytics data similar to that possible in a real factory. One should be able to look at the overall factory performance and be able to drill down to a machine and analyze its performance. It will require a large amount of effort and expertise to build such a virtual factory. This paper describes an effort to build a multiple resolution model of a manufacturing cell. The model provides the ability to study the performance at the cell level or at the machine level. The benefits and limitations of the presented approach and future research directions are also described.

This work simulated some alternatives of dynamic allocation of additional human resources in a company that produces various products from Pupunha palm. Its goal was to increase the average amount of trays produced per day in this line through a hybrid application of discrete event and agent-based simulation. Two different decision-making forms were proposed to find out which workstation should have received an additional operator. The first proposal was made on the level of occupancy of the operators, while the second one was made on the queue size. The computational model was operationally validated by comparing its results with the actual production data of the company.

Symbiotic simulation is a paradigm that emphasizes a close association between a simulation system and a physical system, which is usually beneficial to at least one of them and not necessarily detrimental to the others. Aimed at extending previous work in symbiotic simulation, this paper proposes a framework of symbiotic simulation that can be used to improve the performance of a production system controlled by an enterprise system.

Dynamic models are used to describe the spatio-temporal evolution of complex systems. It is frequently difficult to construct a useful model, especially for emerging situations such as the 2003 SARS outbreak.Here we describe the application of a modern predictor-corrector method – particle filtering – that could enable relatively quick model construction and support on-the-fly correction as empirical data arrives.

The labor-intensive nature of construction projects requires proper management and efficient utilization of labor resources. Improvement of labor productivity can enhance project performance and thereby lead to substantial time and cost savings. Several studies focused on identifying the effect of different factors on labor productivity, whereby the learning curve factor proved of paramount importance. Although previous research efforts developed models to represent the learning curve effect using traditional simulation approaches such as System Dynamics (SD) and Discrete Event Simulation (DES), none of these studies used Agent-Based Modeling (ABM) techniques. This study takes the initial steps and presents work targeted at analyzing the effect of learning on labor productivity using ABM.

In order to cope with the frequent unpredictable changes that may occur in manufacturing systems, and to optimize given performance criteria, the assignment of workers can be decided online in a dynamic manner, whenever the worker is released. Several studies, in the ergonomics literature, have shown that individuals' performances decrease because of their fatigue in work. In manufacturing context, the workers’ fatigue impacts the task durations. Therefore, we propose to solve the online workers assignment problem through a heuristic, which takes this workers' fatigue into consideration, so as to minimize the mean flowtime of jobs.

This research proposes and tests an integrated framework for bottom-up simulation of performance in construction projects. The proposed framework conceptualizes construction projects as systems-of-systems in which the abstraction and micro-simulation of dynamic behaviors are investigated at the base-level consisting of the following elements: human agents, information, and resources.

Projects in mechanized tunneling frequently do not reach their targeted production performance. Reasons are often related to an undersized or disturbed supply-chain management of the surface jobsite. Due to the sensitive interaction of production and logistic processes, planning and analyzing the supply-chain is a challenging task.